Abstract
This paper presents new methods for generation of random Bayesian networks. Such methods can be used to test inference and learning algorithms for Bayesian networks, and to obtain insights on average properties of such networks. Any method that generates Bayesian networks must first generate directed acyclic graphs (the “structure” of the network) and then, for the generated graph, conditional probability distributions. No algorithm in the literature currently offers guarantees concerning the distribution of generated Bayesian networks. Using tools from the theory of Markov chains, we propose algorithms that can generate uniformly distributed samples of directed acyclic graphs. We introduce methods for the uniform generation of multi-connected and singly-connected networks for a given number of nodes; constraints on node degree and number of arcs can be easily imposed. After a directed acyclic graph is uniformly generated, the conditional distributions are produced by sampling Dirichlet distributions.
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References
Caprile, B.: Uniformly Generating Distribution Functions for Discrete Random Variables (2000).
Gamerman, D.: Markov Chain Monte Carlo. Stochastic simulation for Bayesian inference. Texts in Statistical Science Series. Chapman and Hall, London (1997).
Jensen, F. V.: An Introduction to Bayesian Networks. Springer-Verlag, New York (1996).
Melançon, G., Bousque-Melou, M.: Random Generation of Dags for Graph Drawing. Dutch Research Center for Mathematical and Computer Science (CWI). Technical Report INS-R0005 February (2000).
Chartrand, G., Oellermann, O.R.: Applied and Algorithmic Graph Theory. International Series in Pure and Applied Mathematics. McGraw-Hill, New York (1993).
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kauffman (1988).
Ripley, B. D.: Stochastic Simulation. Wiley Series in Probability and mathematical Statistics. John Wiley and Sons, Inc., New York (1987).
Sinclair, A.: Algoritms for Random Generation and Counting: A Markov Chain Approach. Progress in Theoretical Computer Science. Birkhaüser, Boston (1993).
Xiang, Y., Miller, T.: A Well-Behaved Algorithm for Simulating Dependence Structures of bayesian Networks. International Journal of Applied Mathematics, Vol. 1, No. 8 (1999), 923–932.
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© 2002 Springer-Verlag Berlin Heidelberg
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Ide, J.S., Cozman, F.G. (2002). Random Generation of Bayesian Networks. In: Bittencourt, G., Ramalho, G.L. (eds) Advances in Artificial Intelligence. SBIA 2002. Lecture Notes in Computer Science(), vol 2507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36127-8_35
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DOI: https://doi.org/10.1007/3-540-36127-8_35
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